Nonlinear modelling and control of electrically stimulated muscle: A local model network approach

International Journal of Control

Volumn 68, Issue 6, 1997, Pages 1259-1288

  Gollee, H ^a   Hunt, K J ^a,b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b DAIMLER AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONTROL SYSTEM SYNTHESIS; FEEDBACK CONTROL; LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; MUSCLE; NONLINEAR CONTROL SYSTEMS; ROBUSTNESS (CONTROL SYSTEMS); TRANSFER FUNCTIONS;

LOCAL MODEL NETWORKS (LMN);

BIOMECHANICS;

EID: 0031343954     PISSN: 00207179     EISSN: 13665820     Source Type: Journal    
DOI: 10.1080/002071797223028     Document Type: Article

References (46)

1. Astrom, K. J., and Wittenmark, B., 1990, Computer-Controlled Systems. Theory and Design, Second edition (Englewood Cliffs, NJ: Prentice-Hall).
2. Akaike, H., 1974, A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
3. Cacciatore, T. W., and Nowlan, S. J., 1994, Mixtures of controllers for jump linear and non-linear plants. Neural Information Processing Systems 6, pp. 719-726.
4. Donaldson, N. de N., Gollee, H., Hunt, K. J., Jarvis, J. C., and Kwende, M. K. N., 1995, A radial basis function model of muscle stimulated with irregular inter-pulse intervals. Medical Engineering and Physics, 17, 431-441.
5. Franklin, G. F., Powell, J. D., and Emami-Naeini, A. 1994, Feedback Control of Dynamic Systems, third edition (Reading, MA: Addison-Wesley).
6. Friedmann, J. H., 1991, Multivariate adaptive regression splines. Annals of Statistics, 19, 1-141.
7. Gollee, H., Hunt, K. J., Donaldson, N. de N., and Jarvis, J. C., 1997, Modelling of electrically stimulated muscle. Multiple Model Approaches to Modelling and Control, edited by R. Murray-Smith and T. A. Johansen (London: Taylor & Francis), chapter 3.
8. Gollee, H., Hunt, K. J., Donaldson, N. de N., Jarvis, J. C., and Kwende, M. K. N., 1994, A mathematical analogue of electrically stimulated muscle using local model networks. Proceedings 33rd IEEE Conference on Decision and Control, Vol. 2, Lake Buena Vista, FL, pp. 1879-1880.
9. Gray, H., 1995, Gray’s Anatomy: The Anatomical Basis of Medicine and Surgery, 38th edition (New York: Churchill Livingstone).
10. Haber, R., and Unbehauen, H., 1990, Structure identification of nonlinear dynamic systems-a survey on input/output approaches. Automatica, 26, 651-677.
11. Hambrecht, F. T., 1992, A brief history of neural prostheses for motor control of paralyzed extremities. Neural Prostheses. Replacing Motor Function After Disease or Disability, edited by R. B. Stein, P. H. Peckham and D. P. Popovic (Oxford University Press), pp. 3-14.
12. Hill, A. V., 1938, The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London B, 126, 136-195.
13. Hlava'Ckova', K. 1995, An upper estimate of the error of approximation of continuous multivariable functions by KBF networks. Proceedings of the Third European Symposium on Artificial Neural Networks, Brussels, Belgium, pp. 333-340.
14. Hunt, K. J., Haas, R., and Brown, M., 1995, On the functional equivalence of fuzzy inference systems and spline-based networks. International Journal of Neural Systems, 6, 171-184.
15. Hunt, K. J., Haas, R., and Murray-Smith, R., 1996, Extending the functional equivalence of radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks, 7, 776-781.
16. Hunt, K. J., and Johansen, T. A., 1997, Design and analysis of gain-scheduled control using local controller networks. International Journal of Control, 66, 616-651.
17. Huxley, A. F., 1957, Muscle structure and theories of contraction. Progess in Biophysics and Biophysical Chemistry, 7, 257-318.
18. Jacobs, R. A., and Jordan, M. I., 1993, Learning piecewise control strategies in a modular neural network architecture. IEEE Transactions on Systems, Man and Cybernetics, 23, 337-345.
19. Jang, J.-S. R., and Sun, C.-T., 1993, Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks, 4, 156-159.
20. Johansen, T. A., 1994, Operating regime based process modeling and identification. PhD thesis, Department of Engineering Cybernetics, Norwegian Institute of Technology, University of Trondheim, Norway.
21. Johansen, T. A., and Foss, B. A., 1992, A NARMAX model representation for adaptive control based on local models. Modelling, Identification and Control, 13, 25-39.
22. Johansen, T. A., and Foss, B. A., 1993, Constructing NARMAX models using ARMAX models. International Journal of Control, 58, 1125-1153.
23. Jutten, Ch., and Fambon, O., 1995, Pruning methods: a review. Proceedings of the Third European Symposium on Artificial Neural Networks, Brussels, Belgium, pp. 129-140.
24. Kavli, T, 1993, ASMOD-an agorithm for adaptive spline modelling of observation data. International Journal of Control, 58, 1993.
25. Kobetic, R., and Marsolais, E. B., 1994, Synthesis of paraplegic gait with multichannel functional neuromuscular stimulation. IEEE Transactions on Rehabilitation Engineering, 2, 66-79.
26. Kobetic, R., Marsolais, E. B., and Miller, P. C., 1994, Function and strength of electrically stimulated hip flexor muscles in paraplegia. IEEE Transactions on Rehabilitation Engineering, 2, 11-17.
27. Kuipers, B., and Astrom, K. J., 1994, The composition and validation of heterogeneous control laws. Automatica, 30, 233-249.
28. Kwende, M. M. N., Jarvis, J. C., and Salmons, S., 1995, The input-output relations of skeletal muscle. Proceedings of the Royal Society of London B, 261, 193-201.
29. Leontaritis, I. J., and Billings, S. A., 1985, Input-output parametric models for nonlinear systems. Part I: Deterministic non-linear systems. International Journal of Control, 41, 303-328; 1987, Model selection and validation methods for non-linear systems. International Journal of Control, 45, 311-341.
30. Lj ung, L., 1987, System Identification. Theory for the User (Englewood Cliffs, N.J.: Prentice-Hall).
31. Murray-Smith, R., 1994, A local model network approach to nonlinear modelling. PhD thesis, University of Strathclyde, Glasgow, UK.
32. Murray-Smith, R., and Hunt, K. J., 1995, Local model architectures for nonlinear modelling and control. Neural Network Engineering in Dynamic Control Systems, Advances in Industrial Control, edited by K. J. Hunt, G. R. Irwin and K. Warwick (Berlin: Springer-Verlag).
33. Murray-Smith, R., and Johansen, T. A., 1997, Local learning in local model networks. Multiple Model Approaches to Modelling and Control, edited by R. Murray-Smith and T. A. Johansen (London: Taylor & Francis), chapter 7.
34. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., 1992, Numerical Recipes (C): The Art of Scientific Computing, second edition (Cambridge University Press).
35. Reed, R., 1994. Pruning algorithms: a survey. IEEE Transactions on Neural Networks, 4, 740-747.
36. Rugh, W. J., 1991, Analytical framework for gain scheduling. IEEE Control Systems Magazine, 11, 79-84.
37. Salmons, S., and Jarvis, J. C., 1992, Cardiac assistance from skeletal muscle: a critical appraisal of the various approaches. British Heart Journal, 68, 333-338.
38. Shamma, J. S., 1988, Analysis and design of gain scheduled control systems. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA.
39. Shamma, J. S., and Athans, M., 1990, Analysis of gain scheduled control for nonlinear plants. IEEE Transactions on Automatic Control, 35, 898-907; 1991, Guaranteed properties of gain scheduled control for linear parameter-varying plants. Automatica, 27, 559-564; 1991, Gain scheduling: Potential hazards and possible remedies. IEEE Control Systems Magazine, 12(3), 101-107.
40. Shorten, R., and Murray-Smith, R., 1997, Side-effects of normalising basis functions. Multiple Model Approaches to Modelling and Control, edited by R. Murray-Smith and T. A. Johansen (London: Taylor & Francis), chapter 8.
41. Stein, R. B., Peckham, P. H., and Popovic, D. P. (editors), 1992, Neural Prostheses. Replacing Motor Function After Disease or Disability (Oxford University Press).
42. Takagi, T., and Sugeno, M., 1985, Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15, 116-132.
43. Weiss, S. M., and Kulikowski, C. A., 1991, Computer Systems that Learn (San Mateo, CA: Morgan Kaufmann).
44. Winters, J. M., and Woo, S. L.-Y., 1990, Multiple Muscle Systems. Biomechanics and Movement Organization (Berlin: Springer-Verlag).
45. Zahalak, G. I., 1992, An overview of muscle modeling. Neural Prostheses. Replacing Motor Function After Disease or Disability, edited by R. B. Stein, P. H. Peckham and D. P. Popovic (Oxford University Press), pp. 17-57.
46. Zbikowski, R., Hunt, K. J., DzieliNski, A., Murray-Smith, R., and Gawthrop, P. J., 1994, A review of advances in neural adaptive control systems. ESPRIT NACt Project: Technical Report TP-1, Glasgow University and Daimler-Benz Research. http: //www. mech. gla ac. uk/nactftp/nact.html